WO2019218291A1 - Method and device used for filtering positioning data - Google Patents

Method and device used for filtering positioning data Download PDF

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WO2019218291A1
WO2019218291A1 PCT/CN2018/087199 CN2018087199W WO2019218291A1 WO 2019218291 A1 WO2019218291 A1 WO 2019218291A1 CN 2018087199 W CN2018087199 W CN 2018087199W WO 2019218291 A1 WO2019218291 A1 WO 2019218291A1
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filters
positioning data
filter
current time
calculating
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PCT/CN2018/087199
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French (fr)
Chinese (zh)
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于华俊
崔乐
王炜
张明亮
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罗伯特·博世有限公司
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Priority to EP18918812.1A priority Critical patent/EP3796028A4/en
Priority to PCT/CN2018/087199 priority patent/WO2019218291A1/en
Priority to CN201880091126.0A priority patent/CN111837048B/en
Priority to US17/055,494 priority patent/US20210215488A1/en
Publication of WO2019218291A1 publication Critical patent/WO2019218291A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G01S13/726—Multiple target tracking
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S2205/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S2205/01—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations specially adapted for specific applications
    • G01S2205/02—Indoor

Definitions

  • the positioning data prediction value for each of the two filters at the current time is based on the predicted value of the positioning data for the filter at the previous time, and is calculated Calculated for the mixed input of the filter, a given and invariant transition matrix, a given and invariant observation matrix, and the coefficient gain of the current time (eg, but not limited to, using equation (3) ) Calculated).

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

A method and device used for filtering positioning data, the method comprising: receiving positioning data outputted by a positioning engine at a current moment (302); using an interactive multi-model consisting of two different filters to filter positioning data to be processed based on the received positioning data so as to obtain filtered positioning data (306). By using the method and device, the accuracy and robustness of positioning may be improved.

Description

用于对定位数据滤波的方法和装置Method and apparatus for filtering positioning data 技术领域Technical field
本发明涉及定位领域，尤其涉及用于对定位数据滤波的方法、装置和处理设备以及定位设备和计算机可读存储介质。The present invention relates to the field of positioning, and more particularly to a method, apparatus and processing apparatus for filtering positioning data, and positioning apparatus and computer readable storage medium.
背景技术Background technique
室内定位技术是一种用于对位于室内的目标对象(例如，人员、车辆等)进行定位的技术，其在室内的不同位置布置多个信号发射源，然后由定位引擎根据从这些信号发射源中的若干个信号发射源接收的信号连续地计算目标对象的定位数据并输出所计算的定位数据。室内定位技术在许多不同的领域中得到了广泛地应用。Indoor positioning technology is a technology for locating target objects (eg, people, vehicles, etc.) located indoors, which arranges multiple signal transmission sources at different locations in the room, and then is based on the source from these signals. The signals received by the plurality of signal transmitting sources continuously calculate the positioning data of the target object and output the calculated positioning data. Indoor positioning technology is widely used in many different fields.
由于遮挡和各个信号发射源之间的不良同步的缘故，用于室内定位的定位引擎所计算的定位数据经常与目标对象的真实位置有差距并且也不太稳定。为此，现有技术已经提出利用一个滤波器(例如，均值滤波器、卡尔曼滤波器等)来对定位引擎输出的定位数据进行滤波以提供定位的准确性和鲁棒性。然而，目前这种利用一个滤波器来对定位引擎输出的定位数据进行滤波的技术，对定位的准确性和鲁棒性的提高还不太令人满意。Due to the poor synchronization between the occlusion and the individual signal sources, the positioning data calculated by the positioning engine for indoor positioning often differs from the true position of the target object and is also less stable. To this end, the prior art has proposed to utilize a filter (eg, mean filter, Kalman filter, etc.) to filter the positioning data output by the positioning engine to provide positioning accuracy and robustness. However, the current technique of filtering the positioning data output by the positioning engine by using a filter is not satisfactory for the accuracy and robustness of positioning.
发明内容Summary of the invention
考虑到现有技术的以上问题，本发明的实施例提供一种用于对定位数据滤波的方法、装置和处理设备以及定位设备和计算机可读存储介质，其能够提高定位的准确性和鲁棒性。In view of the above problems of the prior art, embodiments of the present invention provide a method, apparatus, and processing apparatus for filtering positioning data, and a positioning apparatus and a computer readable storage medium capable of improving positioning accuracy and robustness Sex.
按照本发明的实施例的一种用于对定位数据滤波的方法，包括：接收定位引擎当前时刻输出的定位数据；以及，利用由两个不同的滤波器组成的交互式多模型对基于所接收的定位数据的待处理定位数 据进行滤波，以得到已滤波的定位数据。A method for filtering positioning data according to an embodiment of the present invention, comprising: receiving positioning data output by a positioning engine at a current time; and using an interactive multi-model pair composed of two different filters based on the received The to-be-processed positioning data of the positioning data is filtered to obtain filtered positioning data.
按照本发明的实施例的一种用于对定位数据滤波的装置，包括：接收模块，用于接收定位引擎当前时刻输出的定位数据；以及，滤波模块，用于利用由两个不同的滤波器组成的交互式多模型对基于所接收的定位数据的待处理定位数据进行滤波，以得到已滤波的定位数据。An apparatus for filtering positioning data according to an embodiment of the present invention includes: a receiving module, configured to receive positioning data output by a positioning engine at a current time; and a filtering module configured to utilize two different filters The composed interactive multi-model filters the to-be-processed positioning data based on the received positioning data to obtain filtered positioning data.
按照本发明的实施例的一种用于对定位数据滤波的处理设备，包括：处理器；以及，存储器，其存储有可执行指令，所述可执行指令当被执行时使得所述处理器执行前述方法所包括的操作。A processing apparatus for filtering positioning data, comprising: a processor; and a memory storing executable instructions that, when executed, cause the processor to execute, in accordance with an embodiment of the present invention The operations included in the foregoing methods.
按照本发明的实施例的一种机器可读存储介质，其上具有可执行指令，当所述可执行指令被执行时，使得机器执行前述方法所包括的操作。A machine readable storage medium having executable instructions thereon that, when executed, cause a machine to perform the operations included in the foregoing methods, in accordance with an embodiment of the present invention.
按照本发明的实施例的一种定位设备，包括：定位引擎，用于连续地计算目标对象的定位数据并输出所计算的定位数据；以及，前述的处理设备。A positioning apparatus according to an embodiment of the present invention includes: a positioning engine for continuously calculating positioning data of a target object and outputting the calculated positioning data; and the aforementioned processing device.
本发明的实施例的方案利用由两个滤波器组成的交互式多模型来对定位引擎输出的定位数据进行滤波，由于与单个滤波器相比，组合两个滤波器的滤波结果而得到的定位更加准确和稳定，因此，与现有技术相比，本发明的实施例的方案能够提高定位的准确性和鲁棒性。The scheme of the embodiment of the present invention utilizes an interactive multi-model composed of two filters to filter the positioning data output by the positioning engine, and the positioning obtained by combining the filtering results of the two filters is compared with a single filter. It is more accurate and stable, and therefore, the solution of the embodiment of the present invention can improve the accuracy and robustness of positioning as compared with the prior art.
附图说明DRAWINGS
本发明的其它特征、特点、益处和优点通过以下结合附图的详细描述将变得更加显而易见。其中：Other features, characteristics, advantages and advantages of the present invention will become more apparent from the detailed description of the appended claims. among them:
图1示出了按照本发明的一个实施例的定位设备的结构示意图；FIG. 1 is a block diagram showing the structure of a positioning apparatus according to an embodiment of the present invention; FIG.
图2示出了按照本发明的一个实施例的用于对定位数据滤波的方法的总体流程图；2 shows a general flow diagram of a method for filtering positioning data in accordance with one embodiment of the present invention;
图3示出了按照本发明的一个实施例的用于对定位数据滤波的方法的示意图；3 shows a schematic diagram of a method for filtering positioning data in accordance with one embodiment of the present invention;
图4示出了按照本发明的一个实施例的用于对定位数据滤波的 装置的示意图；Figure 4 shows a schematic diagram of an apparatus for filtering positioning data in accordance with one embodiment of the present invention;
图5示出了按照本发明的一个实施例的用于对定位数据滤波的处理设备的示意图；以及Figure 5 shows a schematic diagram of a processing device for filtering positioning data in accordance with one embodiment of the present invention;
图6示出了按照本发明的一个实施例的定位设备的示意图。Figure 6 shows a schematic diagram of a positioning device in accordance with one embodiment of the present invention.
具体实施方式Detailed ways
下面将参考附图详细描述本发明的各个实施例。Various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
图1示出了按照本发明的一个实施例的定位设备的结构示意图。如图1所示，定位设备10可以包括定位引擎20和处理设备30。定位引擎20用于例如根据从放置在室内的不同位置的多个信号发射源中的若干信号发射源接收的信号来连续地计算位于室内的目标对象T的定位数据并输出所计算的定位数据。处理设备30用于利用由卡尔曼一阶(first order：FO)滤波器和卡尔曼匀速(constant velocity：CV)滤波器组成的交互式多模型(Interacting Multiple Model，简称IMM)P来对定位引擎20各个时刻输出的定位数据进行滤波，以得到各个时刻的已滤波的定位数据，这将在下面参考图2详细说明。Figure 1 shows a block diagram of a positioning apparatus in accordance with one embodiment of the present invention. As shown in FIG. 1, the positioning device 10 can include a positioning engine 20 and a processing device 30. The positioning engine 20 is for continuously calculating positioning data of a target object T located indoors and outputting the calculated positioning data, for example, based on signals received from a plurality of signal transmission sources among a plurality of signal transmission sources placed at different positions in the room. The processing device 30 is configured to use an Interacting Multiple Model (IMM) P composed of a Kalman first-order (FO) filter and a constant velocity (CV) filter to locate the positioning engine. The positioning data outputted at each time is filtered to obtain filtered positioning data at each moment, which will be described in detail below with reference to FIG.
图2示出了按照本发明的一个实施例的用于对定位数据滤波的方法的总体流程图。图2所示的方法200由处理设备30来实现。2 shows a general flow diagram of a method for filtering positioning data in accordance with one embodiment of the present invention. The method 200 shown in FIG. 2 is implemented by the processing device 30.
如图2所示，在方框202，处理设备30接收定位引擎20当前时刻输出的定位数据。为了便于描述，假设当前时刻是k时刻，将定位引擎20当前时刻输出的定位数据表示为
Figure PCTCN2018087199-appb-000001
其也被称为由硬件***测量得到的观测值。 As shown in FIG. 2, at block 202, processing device 30 receives positioning data that is output by positioning engine 20 at the current time. For convenience of description, it is assumed that the current time is the k time, and the positioning data output by the positioning engine 20 at the current time is expressed as
Figure PCTCN2018087199-appb-000001
It is also referred to as the observations measured by the hardware system.
在方框206，处理设备30对所接收的定位数据
Figure PCTCN2018087199-appb-000002
进行预处理，得到待处理定位数据
Figure PCTCN2018087199-appb-000003
预处理的目的在于消除其明显偏离最近接收到的定位数据的那些异常的定位数据。例如但不局限于，如果定位数据
Figure PCTCN2018087199-appb-000004
被判定是异常的定位数据，则计算在当前时刻之前的多个时刻接收到的定位数据的平均值，作为待处理定位数据
Figure PCTCN2018087199-appb-000005
以及，如果定位数据
Figure PCTCN2018087199-appb-000006
被判定是正常的定位数据，则待处理定位数据
Figure PCTCN2018087199-appb-000007
是定位数据
Figure PCTCN2018087199-appb-000008
At block 206, the processing device 30 pairs the received positioning data
Figure PCTCN2018087199-appb-000002
Perform pre-processing to obtain pending positioning data
Figure PCTCN2018087199-appb-000003
The purpose of the pre-processing is to eliminate those abnormal positioning data that deviate significantly from the most recently received positioning data. For example but not limited to if positioning data
Figure PCTCN2018087199-appb-000004
If it is determined that the positioning data is abnormal, the average value of the positioning data received at a plurality of times before the current time is calculated as the positioning data to be processed.
Figure PCTCN2018087199-appb-000005
And if positioning data
Figure PCTCN2018087199-appb-000006
If it is determined that it is normal positioning data, the positioning data to be processed
Figure PCTCN2018087199-appb-000007
Is positioning data
Figure PCTCN2018087199-appb-000008
在方框210，处理设备30利用等式(1)计算当前时刻的交互式多模型P中的卡尔曼FO滤波器的滤波结果的占比
Figure PCTCN2018087199-appb-000009
(其表示在当前时刻的交互式多模型P中的卡尔曼FO滤波器和卡尔曼CV滤波器的滤波结果的总和中，当前时刻的交互式多模型P中的卡尔曼FO滤波器的滤波结果占据的比例)和当前时刻的交互式多模型P中的卡尔曼CV滤波器的滤波结果的占比
Figure PCTCN2018087199-appb-000010
(其表示在当前时刻的交互式多模型P中的卡尔曼FO滤波器和卡尔曼CV滤波器的滤波结果的总和中，当前时刻的交互式多模型P中的卡尔曼CV滤波器的滤波结果占据的比例)。 At block 210, the processing device 30 calculates the proportion of the filtered results of the Kalman FO filter in the interactive multi-model P at the current time using equation (1).
Figure PCTCN2018087199-appb-000009
(It represents the filtering result of the Kalman FO filter in the interactive multi-model P at the current time in the sum of the filtering results of the Kalman FO filter and the Kalman CV filter in the interactive multi-model P at the current time. Proportion of the filtered results of the Kalman CV filter in the interactive multi-model P at the current time
Figure PCTCN2018087199-appb-000010
(It represents the filtering result of the Kalman CV filter in the interactive multi-model P at the current time in the sum of the filtering results of the Kalman FO filter and the Kalman CV filter in the interactive multi-model P at the current time. Occupied ratio).
Figure PCTCN2018087199-appb-000011
Figure PCTCN2018087199-appb-000011
其中，M 1表示交互式多模型P中的卡尔曼FO滤波器的马尔科夫链转移概率，M 2表示交互式多模型P中的卡尔曼CV滤波器的马尔科夫链转移概率，M 1和M 2是已给定的常数，
Figure PCTCN2018087199-appb-000012
表示上一时刻(即k-1时刻)的交互式多模型P选择卡尔曼FO滤波器的概率，
Figure PCTCN2018087199-appb-000013
表示上一时刻的交互式多模型P选择卡尔曼CV滤波器的概率，以及，
Figure PCTCN2018087199-appb-000014
Where M 1 represents the Markov chain transition probability of the Kalman FO filter in the interactive multi-model P, and M 2 represents the Markov chain transition probability of the Kalman CV filter in the interactive multi-model P, M 1 And M 2 is a given constant,
Figure PCTCN2018087199-appb-000012
The probability of selecting the interactive multi-model P-selected Kalman FO filter at the previous moment (ie, time k-1),
Figure PCTCN2018087199-appb-000013
The probability of representing the interactive multi-model P-selected Kalman CV filter at the previous moment, and,
Figure PCTCN2018087199-appb-000014
在方框214，处理设备30利用等式(2)分别计算针对交互式多模型P中的卡尔曼FO滤波器的混合输入InputMixing A1和针对交互式多模型P中的卡尔曼CV滤波器的混合输入InputMixing A2。 At block 214, the processing device 30 calculates a mixture of the mixed input InputMixing A1 for the Kalman FO filter in the interactive multi-model P and the Kalman CV filter for the interactive multi-model P, respectively, using equation (2). Enter InputMixing A2 .
Figure PCTCN2018087199-appb-000015
Figure PCTCN2018087199-appb-000015
其中，
Figure PCTCN2018087199-appb-000016
表示上一时刻的交互式多模型P中的卡尔曼FO滤波器输出的初步滤波结果，以及，
Figure PCTCN2018087199-appb-000017
表示上一时刻的交互式多模型P中的卡尔曼CV滤波器输出的初步滤波结果。 among them,
Figure PCTCN2018087199-appb-000016
Representing the preliminary filtering result of the Kalman FO filter output in the interactive multi-model P at the previous moment, and
Figure PCTCN2018087199-appb-000017
Represents the preliminary filtering result of the Kalman CV filter output in the interactive multi-model P at the previous moment.
在方框218，处理设备30利用等式(3)计算当前时刻的用于交互式多模型P中的卡尔曼FO滤波器的定位数据预测值
Figure PCTCN2018087199-appb-000018
和当前时 刻的用于交互式多模型P中的卡尔曼CV滤波器的定位数据预测值
Figure PCTCN2018087199-appb-000019
At block 218, the processing device 30 calculates the positioning data prediction values for the Kalman FO filter in the interactive multi-model P at the current time using equation (3).
Figure PCTCN2018087199-appb-000018
Positioning data prediction values for the Kalman CV filter in the interactive multi-model P at the current time
Figure PCTCN2018087199-appb-000019
Figure PCTCN2018087199-appb-000020
Figure PCTCN2018087199-appb-000020
其中，F k表示给定且不变的转移矩阵，H表示给定且不变的观测矩阵，
Figure PCTCN2018087199-appb-000021
表示上一时刻的用于交互式多模型P中的卡尔曼FO滤波器的定位数据预测值，
Figure PCTCN2018087199-appb-000022
表示上一时刻的用于交互式多模型P中的卡尔曼CV滤波器的定位数据预测值，K k表示当前时刻的系数增益，
Figure PCTCN2018087199-appb-000023
R表示给定且不变的测量噪声的协方差，
Figure PCTCN2018087199-appb-000024
表示当前时刻的先验协方差矩阵，
Figure PCTCN2018087199-appb-000025
Q表示给定且不变的处理噪声协方差，
Figure PCTCN2018087199-appb-000026
表示上一时刻的的先验协方差矩阵。 Where F k represents a given and invariant transition matrix, and H represents a given and invariant observation matrix,
Figure PCTCN2018087199-appb-000021
Representing the predicted value of the positioning data for the Kalman FO filter in the interactive multi-model P at the previous moment,
Figure PCTCN2018087199-appb-000022
Representing the predicted value of the positioning data for the Kalman CV filter in the interactive multi-model P at the previous moment, K k represents the coefficient gain of the current time,
Figure PCTCN2018087199-appb-000023
R represents the covariance of a given and constant measurement noise,
Figure PCTCN2018087199-appb-000024
a priori covariance matrix representing the current moment,
Figure PCTCN2018087199-appb-000025
Q represents the given and constant processing noise covariance,
Figure PCTCN2018087199-appb-000026
Represents the a priori covariance matrix of the previous moment.
在方框222，处理设备30使用交互式多模型P中的卡尔曼FO滤波器对定位数据预测值
Figure PCTCN2018087199-appb-000027
进行滤波，所得到的滤波结果作为当前时刻的交互式多模型P中的卡尔曼FO滤波器的初步滤波结果
Figure PCTCN2018087199-appb-000028
以及，使用交互式多模型P中的卡尔曼CV滤波器对定位数据预测值
Figure PCTCN2018087199-appb-000029
进行滤波，所得到的滤波结果作为当前时刻的交互式多模型P中的卡尔曼CV滤波器的初步滤波结果
Figure PCTCN2018087199-appb-000030
At block 222, processing device 30 uses the Kalman FO filter in interactive multi-model P to predict the positioning data.
Figure PCTCN2018087199-appb-000027
Filtering is performed, and the obtained filtering result is used as a preliminary filtering result of the Kalman FO filter in the interactive multi-model P at the current time.
Figure PCTCN2018087199-appb-000028
And using the Kalman CV filter in the interactive multi-model P to predict the positioning data
Figure PCTCN2018087199-appb-000029
Filtering is performed, and the obtained filtering result is used as a preliminary filtering result of the Kalman CV filter in the interactive multi-model P at the current time.
Figure PCTCN2018087199-appb-000030
在方框226，处理设备30利用等式(4)计算针对交互式多模型P中的卡尔曼FO滤波器的滤波参数值eK A1和针对交互式多模型P中的卡尔曼CV滤波器的滤波参数值eK A2。 At block 226, processing device 30 calculates a filter parameter value eK A1 for the Kalman FO filter in the interactive multi-model P and a filter for the Kalman CV filter in the interactive multi-model P using equation (4) The parameter value is eK A2 .
Figure PCTCN2018087199-appb-000031
Figure PCTCN2018087199-appb-000031
其中，P noise表示给定且不变的测量噪声的协方差。 Where P noise represents the covariance of the given and constant measurement noise.
在方框230，处理设备30利用等式(5)计算当前时刻的交互式多模型P选择卡尔曼FO滤波器的概率
Figure PCTCN2018087199-appb-000032
和当前时刻的交互式多模型P选择卡尔曼CV滤波器的概率
Figure PCTCN2018087199-appb-000033
At block 230, processing device 30 calculates the probability of interactive multi-model P-selected Kalman FO filter at the current time using equation (5)
Figure PCTCN2018087199-appb-000032
Probability of selecting a Kalman CV filter with an interactive multi-model P at the current moment
Figure PCTCN2018087199-appb-000033
Figure PCTCN2018087199-appb-000034
Figure PCTCN2018087199-appb-000034
在方框234，处理设备30利用等式(6)计算交互式多模型P的滤波输出Output，作为交互式多模型P对当前时刻的待处理定位数据
Figure PCTCN2018087199-appb-000035
的滤波结果。 At block 234, the processing device 30 calculates the filtered output Output of the interactive multi-model P using equation (6) as the interactive multi-model P versus the current positional pending data.
Figure PCTCN2018087199-appb-000035
Filter results.
Figure PCTCN2018087199-appb-000036
Figure PCTCN2018087199-appb-000036
然后，在方框234之后，流程返回到方框202，以对定位引擎下一时刻(即k+1时刻)输出的定位数据
Figure PCTCN2018087199-appb-000037
进行滤波。 Then, after block 234, the flow returns to block 202 to locate the positioning data for the next time the positioning engine (i.e., time k+1).
Figure PCTCN2018087199-appb-000037
Filtering is performed.
本实施例的方案利用由两个滤波器(即，卡尔曼FO滤波器和卡尔曼CV滤波器)组成的交互式多模型来对定位引擎输出的定位数据进行滤波，由于与单个滤波器相比，组合两个滤波器的滤波结果而得到的定位更加准确和稳定，因此，本实施例的方案能够提高定位的准确性和鲁棒性。The scheme of this embodiment utilizes an interactive multi-model consisting of two filters (ie, a Kalman FO filter and a Kalman CV filter) to filter the positioning data output by the positioning engine, as compared to a single filter. The positioning obtained by combining the filtering results of the two filters is more accurate and stable. Therefore, the solution of the embodiment can improve the accuracy and robustness of the positioning.
其它变型Other variants
本领域技术人员将理解，虽然在上面的实施例中，当前时刻的用于交互式多模型P中的卡尔曼FO滤波器的定位数据预测值和当前时刻的用于交互式多模型P中的卡尔曼CV滤波器的定位数据预测值是利用等式(3)计算得到的，然而，本发明并不局限于此。在本发明的其它一些实施例中，也可以将利用等式(4)计算的针对交互式多模型P中的卡尔曼FO滤波器的滤波参数值eK A1，作为当前时刻的用于交互式多模型P中的卡尔曼FO滤波器的定位数据预测值，以及，将利用等式(4)计算的针对交互式多模型P中的卡尔曼CV滤波器 的滤波参数值eK A2，作为当前时刻的用于交互式多模型P中的卡尔曼CV滤波器的定位数据预测值，在这种情况下，利用等式(3)计算的
Figure PCTCN2018087199-appb-000038
和
Figure PCTCN2018087199-appb-000039
分别是当前时刻的用于交互式多模型P中的卡尔曼FO滤波器的辅助计算值和当前时刻的用于交互式多模型P中的卡尔曼CV滤波器的辅助计算值。 Those skilled in the art will appreciate that although in the above embodiments, the positioning data prediction values for the Kalman FO filter in the interactive multi-model P at the current time and the current time are used in the interactive multi-model P The predicted value of the positioning data of the Kalman CV filter is calculated using Equation (3), however, the present invention is not limited thereto. In some other embodiments of the present invention, the filter parameter value eK A1 for the Kalman FO filter in the interactive multi-model P calculated by using equation (4) may also be used as the current time for interactive multi-induction. The positioning data prediction value of the Kalman FO filter in the model P, and the filter parameter value eK A2 for the Kalman CV filter in the interactive multi-model P calculated using Equation (4) as the current time Positioning data prediction value for the Kalman CV filter in the interactive multi-model P, in this case, calculated using equation (3)
Figure PCTCN2018087199-appb-000038
with
Figure PCTCN2018087199-appb-000039
The auxiliary calculation values for the Kalman FO filter in the interactive multi-model P at the current time and the auxiliary calculation values for the Kalman CV filter in the interactive multi-model P at the current time are respectively.
本领域技术人员将理解，虽然在上面的实施例中，方法200包括方框206以对所接收的定位数据
Figure PCTCN2018087199-appb-000040
进行预处理，然而，本发明并不局限于此。在本发明的其它一些实施例中，方法200也可以不包括方框206，在这种情况下，待处理定位数据
Figure PCTCN2018087199-appb-000041
就是所接收的定位数据
Figure PCTCN2018087199-appb-000042
Those skilled in the art will appreciate that while in the above embodiments, method 200 includes block 206 for receiving the received positioning data.
Figure PCTCN2018087199-appb-000040
The pretreatment is performed, however, the invention is not limited thereto. In some other embodiments of the invention, method 200 may also not include block 206, in which case the location data to be processed
Figure PCTCN2018087199-appb-000041
Is the received positioning data.
Figure PCTCN2018087199-appb-000042
本领域技术人员将理解，虽然在上面的实施例中，交互式多模型P由卡尔曼FO滤波器和卡尔曼CV滤波器组成，然而，本发明并不局限于此。在本发明的其它一些实施例中，交互式多模型P可以例如由卡尔曼FO滤波器和卡尔曼CV滤波器的其中一个和另一个其它的滤波器(例如，均值滤波器等)组成，或者，例如由除了卡尔曼FO滤波器和卡尔曼CV滤波器之外的其它两个滤波器来组成。Those skilled in the art will appreciate that although in the above embodiment, the interactive multi-model P is composed of a Kalman FO filter and a Kalman CV filter, the present invention is not limited thereto. In some other embodiments of the present invention, the interactive multi-model P may be composed, for example, of one of a Kalman FO filter and a Kalman CV filter and another filter (eg, an averaging filter, etc.), or For example, it consists of two other filters than the Kalman FO filter and the Kalman CV filter.
本领域技术人员将理解，本发明的方案不但适用于室内定位的情形，也适用于室外定位的情形。Those skilled in the art will appreciate that the solution of the present invention is applicable not only to indoor positioning but also to outdoor positioning.
图3示出了按照本发明的一个实施例的用于对定位数据滤波的方法的流程图。如图3所示的方法300可以由处理设备30或其它合适的设备来实现。3 shows a flow chart of a method for filtering positioning data in accordance with one embodiment of the present invention. Method 300 as shown in FIG. 3 can be implemented by processing device 30 or other suitable device.
如图3所示，方法300可以包括，在方框302，接收定位引擎当前时刻输出的定位数据。As shown in FIG. 3, method 300 can include, at block 302, receiving positioning data output by the positioning engine at a current time.
方法300还可以包括，在方框306，利用由两个不同的滤波器组成的交互式多模型对基于所接收的定位数据的待处理定位数据进行滤波，以得到已滤波的定位数据。这里，所述待处理定位数据可以是所接收的定位数据或对所接收的定位数据进行预处理后得到的定位数据。The method 300 can also include, at block 306, filtering the to-be-processed positioning data based on the received positioning data using an interactive multi-model consisting of two different filters to obtain filtered positioning data. Here, the to-be-processed positioning data may be the received positioning data or the positioning data obtained by pre-processing the received positioning data.
在第一方面，方框306可以包括：获取所述当前时刻的所述两个 滤波器各自的初步滤波结果，其与所述待处理定位数据相关(例如但不局限于，通过方框210-222来实现)；计算所述当前时刻的所述两个滤波器各自的被选择概率，其中，所述当前时刻的每一个滤波器的被选择概率表示在所述当前时刻处所述交互式多模型选择该滤波器的概率(例如但不局限于，通过方框210-230来实现)；以及，计算所述当前时刻的所述两个滤波器各自的初步滤波结果与被选择概率的乘积之和，作为所述已滤波的定位数据(例如但不局限于，利用等式(6)计算得到)。In a first aspect, block 306 can include obtaining a respective preliminary filtering result of the two filters at the current time, which is related to the to-be-processed positioning data (eg, but not limited to, by block 210- 222. The method calculates a selected probability of each of the two filters at the current time, wherein a selected probability of each filter of the current time indicates that the interactive time is greater at the current time The probability of the model selecting the filter (such as, but not limited to, implemented by blocks 210-230); and calculating a product of the respective filtered results of the two filters at the current time and the selected probability And as the filtered positioning data (for example, but not limited to, calculated using equation (6)).
在第二方面，所述获取所述当前时刻的所述两个滤波器各自的初步滤波结果包括：基于所述两个滤波器各自的马尔科夫链转移概率和在所述当前时刻之前的上一时刻的所述两个滤波器各自的被选择概率，计算所述当前时刻的所述两个滤波器各自的滤波结果占比(例如但不局限于，利用等式(1)计算得到)，其中，所述当前时刻的所述两个滤波器中的任一滤波器的滤波结果占比表示在所述当前时刻的所述两个滤波器的滤波结果总和中，所述当前时刻的该任一滤波器的滤波结果所占据的比例；计算针对所述两个滤波器各自的混合输入(例如但不局限于，利用等式(2)计算得到)，其中，针对每一个滤波器的混合输入是基于所述待处理定位数据、所述上一时刻的该滤波器的初步滤波结果、所述两个滤波器各自的马尔科夫链转移概率和所述上一时刻的所述两个滤波器各自的被选择概率而计算的；计算所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值(例如但不局限于，利用等式(3)计算得到)；以及，通过利用所述两个滤波器中的每一个滤波器对所述当前时刻的用于该滤波器的定位数据预测值进行滤波，获得所述当前时刻的所述两个滤波器各自的初步滤波结果(例如但不局限于，通过方框222来实现)。In a second aspect, the obtaining the preliminary filtering result of each of the two filters at the current moment comprises: based on a Markov chain transition probability of each of the two filters and before the current moment a selected probability of each of the two filters at a time, calculating a ratio of filtering results of the two filters at the current time (for example, but not limited to, calculated using equation (1)), The ratio of the filtering result of any one of the two filters at the current time is represented by the sum of the filtering results of the two filters at the current time, the current time a ratio of the filtering result of a filter; calculating a mixed input for each of the two filters (for example, but not limited to, calculated using equation (2)), wherein the mixed input for each filter Is based on the to-be-processed positioning data, a preliminary filtering result of the filter at the last moment, a Markov chain transition probability of each of the two filters, and the two filters at the last moment Calculated for each selected probability; calculating a predicted value of the positioning data for each of the two filters at the current time (eg, but not limited to, calculated using equation (3)) And obtaining, by using each of the two filters, the positioning data prediction value for the current time at the current time to obtain the respective two filters of the current time Preliminary filtering results (such as, but not limited to, implemented by block 222).
在第三方面，所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值是基于所述上一时刻的用于该滤波器的定位数据预测值、所计算的针对该滤波器的混合输入、给定且不变的转移矩阵、给定且不变的观测矩阵、以及所述当前时刻的系数增益而计算的(例如但不局限于，利用等式(3)计算得到)。In a third aspect, the positioning data prediction value for each of the two filters at the current time is based on the predicted value of the positioning data for the filter at the previous time, and is calculated Calculated for the mixed input of the filter, a given and invariant transition matrix, a given and invariant observation matrix, and the coefficient gain of the current time (eg, but not limited to, using equation (3) ) Calculated).
在第四方面，所述计算所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值包括：计算所述当前时刻的用于所述两个滤波器中的每一个滤波器的辅助计算值，其是基于所述上一时刻的用于该滤波器的辅助计算值、所计算的针对该滤波器的混合输入、给定且不变的转移矩阵、给定且不变的观测矩阵、以及所述当前时刻的系数增益而计算的(例如但不局限于，利用等式(3)计算得到)；以及，确定所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值，其是基于所述待处理定位数据、所述当前时刻的用于该滤波器的辅助计算值和给定且不变的测量噪声的协方差而计算的(例如但不局限于，利用等式(4)计算得到)。In a fourth aspect, the calculating the positioning data prediction value for each of the two filters at the current time comprises: calculating the current time for the two filters An auxiliary calculated value for each filter based on the auxiliary calculated value for the filter at the last moment, the calculated mixed input for the filter, a given and constant transfer matrix, given And an invariant observation matrix, and a coefficient gain of the current time, calculated (for example, but not limited to, calculated using equation (3)); and determining the current time for the two filters a positioning data prediction value for each filter in the device based on the to-be-processed positioning data, the auxiliary calculated value for the filter at the current time, and the covariance of the given and constant measurement noise. Calculated (for example, but not limited to, calculated using equation (4)).
在第五方面，所述计算当前时刻的所述两个滤波器各自的被选择概率包括：计算针对所述两个滤波器各自的滤波参数值，其中，针对所述两个滤波器中的每一个滤波器的滤波参数值是基于所述待处理定位数据、所述当前时刻的用于该滤波器的定位数据预测值和给定且不变的测量噪声的协方差而计算的(例如但不局限于，利用等式(4)计算得到)；以及，基于针对所述两个滤波器各自的滤波参数值和所述当前时刻的所述两个滤波器各自的滤波结果占比，确定所述当前时刻的所述两个滤波器各自的被选择概率(例如但不局限于，利用等式(5)计算得到)。In a fifth aspect, the calculating the selected probability of each of the two filters at the current time comprises: calculating a filter parameter value for each of the two filters, wherein for each of the two filters The filter parameter value of a filter is calculated based on the to-be-processed positioning data, the predicted value of the positioning data for the filter at the current time, and the covariance of the given and constant measurement noise (eg, but not Limited to, calculated using equation (4); and determining, based on a respective filter parameter value for each of the two filters and a filter result ratio of each of the two filters at the current time The selected probability of each of the two filters at the current time (such as, but not limited to, calculated using equation (5)).
在第六方面，方法200还可以包括：对所述接收的定位数据进行预处理，以得到所述待处理定位数据(例如但不局限于，通过方框206来实现)。In a sixth aspect, the method 200 can further include pre-processing the received positioning data to obtain the to-be-processed positioning data (such as, but not limited to, implemented by block 206).
图4示出了按照本发明的一个实施例的用于对定位数据滤波的装置的示意图。图4所示的装置400可以利用软件、硬件或软硬件结合的方式来执行。图4所示的装置400例如可以安装在处理设备30或其它合适的设备中。4 shows a schematic diagram of an apparatus for filtering positioning data in accordance with one embodiment of the present invention. The apparatus 400 shown in FIG. 4 can be implemented in a combination of software, hardware, or a combination of software and hardware. The apparatus 400 shown in Figure 4 can be mounted, for example, in the processing device 30 or other suitable device.
如图4所示，装置400可以包括接收模块402和滤波模块406。接收模块402用于接收定位引擎当前时刻输出的定位数据。滤波模块406用于利用由两个不同的滤波器组成的交互式多模型对基于所接收 的定位数据的待处理定位数据进行滤波，以得到已滤波的定位数据。As shown in FIG. 4, apparatus 400 can include a receiving module 402 and a filtering module 406. The receiving module 402 is configured to receive positioning data that is output by the positioning engine at the current time. The filtering module 406 is configured to filter the to-be-processed positioning data based on the received positioning data by using an interactive multi-model composed of two different filters to obtain filtered positioning data.
在第一方面，滤波模块406包括：获取模块，用于获取所述当前时刻的所述两个滤波器各自的初步滤波结果，其与所述待处理定位数据相关；第一计算模块，用于计算所述当前时刻的所述两个滤波器各自的被选择概率，其中，所述当前时刻的每一个滤波器的被选择概率表示在所述当前时刻处所述交互式多模型选择该滤波器的概率；以及，第二计算模块，用于计算所述当前时刻的所述两个滤波器各自的初步滤波结果和被选择概率的乘积之和，作为所述已滤波的定位数据。In a first aspect, the filtering module 406 includes: an obtaining module, configured to acquire a preliminary filtering result of each of the two filters at the current moment, which is related to the to-be-processed positioning data; and a first calculating module, configured to: Calculating a selected probability of each of the two filters at the current time, wherein a selected probability of each filter of the current time indicates that the interactive multi-model selects the filter at the current time And a second calculation module, configured to calculate a sum of products of respective preliminary filtering results and selected probabilities of the two filters at the current moment as the filtered positioning data.
在第二方面，所述获取模块包括：第三计算模块，用于基于所述两个滤波器各自的马尔科夫链转移概率和在所述当前时刻之前的上一时刻的所述两个滤波器各自的被选择概率，计算所述当前时刻的所述两个滤波器各自的滤波结果占比，其中，所述当前时刻的所述两个滤波器中的任一滤波器的滤波结果占比表示在所述当前时刻的所述两个滤波器的滤波结果总和中，所述当前时刻的该任一滤波器的滤波结果所占据的比例；第四计算模块，用于计算针对所述两个滤波器各自的混合输入，其中，针对每一个滤波器的混合输入是基于所述待处理定位数据、所述上一时刻的该滤波器的初步滤波结果、所述两个滤波器各自的马尔科夫链转移概率和所述上一时刻的所述两个滤波器各自的被选择概率而计算的；第五计算模块，用于计算所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值；以及，获得模块，用于通过利用所述两个滤波器中的每一个滤波器对所述当前时刻的用于该滤波器的定位数据预测值进行滤波，获得所述当前时刻的所述两个滤波器各自的初步滤波结果。In a second aspect, the obtaining module includes: a third calculating module, configured to perform, according to a respective Markov chain transition probability of the two filters and the two filters at a previous moment before the current moment The respective selected probabilities of the respective ones of the two filters are calculated at the current time, wherein the filtering results of any one of the two filters at the current time are proportioned a ratio representing a filter result of the filter of the current time at the current time, and a fourth calculation module, configured to calculate for the two a respective mixed input of the filter, wherein the mixed input for each filter is based on the to-be-processed positioning data, the preliminary filtering result of the filter at the last moment, and the respective Marcos of the two filters Calculated by the probability of the chain transition and the selected probability of each of the two filters at the last moment; a fifth calculation module, configured to calculate the current time for the two filters a positioning data prediction value for each of the filters; and an obtaining module for predicting the positioning data for the current time of the filter by using each of the two filters Filtering is performed to obtain a preliminary filtering result of each of the two filters at the current moment.
在第三方面，所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值是基于所述上一时刻的用于该滤波器的定位数据预测值、所计算的针对该滤波器的混合输入、给定且不变的转移矩阵、给定且不变的观测矩阵、以及所述当前时刻的系数增益而计算的。In a third aspect, the positioning data prediction value for each of the two filters at the current time is based on the predicted value of the positioning data for the filter at the previous time, and is calculated Calculated for the mixed input of the filter, the given and invariant transition matrix, the given and invariant observation matrix, and the coefficient gain of the current instant.
在第四方面，所述第五计算模块包括：第六计算模块，用于计算 所述当前时刻的用于所述两个滤波器中的每一个滤波器的辅助计算值，其是基于所述上一时刻的用于该滤波器的辅助计算值、所计算的针对该滤波器的混合输入、给定且不变的转移矩阵、给定且不变的观测矩阵、以及所述当前时刻的系数增益而计算的；以及，第一确定模块，用于确定所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值，其是基于所述待处理定位数据、所述当前时刻的用于该滤波器的辅助计算值和给定且不变的测量噪声的协方差而计算的。In a fourth aspect, the fifth calculation module includes: a sixth calculation module, configured to calculate an auxiliary calculation value for each of the two filters at the current moment, which is based on the The auxiliary calculated value for the filter at the previous moment, the calculated mixed input for the filter, the given and invariant transition matrix, the given and invariant observation matrix, and the coefficients of the current moment Calculated by the gain; and a first determining module, configured to determine a positioning data prediction value for each of the two filters at the current time, based on the to-be-processed positioning data, The current time is calculated for the auxiliary calculated value of the filter and the covariance of the given and constant measurement noise.
在第五方面，所述第一计算模块包括：第七计算模块，用于计算针对所述两个滤波器各自的滤波参数值，其中，针对所述两个滤波器中的每一个滤波器的滤波参数值是基于所述待处理定位数据、所述当前时刻的用于该滤波器的定位数据预测值和给定且不变的测量噪声的协方差而计算的；以及，第二确定模块，用于基于针对所述两个滤波器各自的滤波参数值和所述当前时刻的所述两个滤波器各自的滤波结果占比，确定所述当前时刻的所述两个滤波器各自的被选择概率。In a fifth aspect, the first calculation module includes: a seventh calculation module, configured to calculate a filter parameter value for each of the two filters, wherein, for each of the two filters The filter parameter value is calculated based on the to-be-processed positioning data, the positioning data prediction value for the filter at the current time, and the covariance of the given and constant measurement noise; and a second determining module, Determining, respectively, that each of the two filters of the current time is selected based on a respective filter result ratio of each of the two filters for the filter values of the two filters and the current time Probability.
在第六方面，装置400还可以包括：预处理模块，用于对所述接收的定位数据进行预处理，以得到所述待处理定位数据。In a sixth aspect, the apparatus 400 may further include: a pre-processing module, configured to perform pre-processing on the received positioning data to obtain the to-be-processed positioning data.
图5示出了按照本发明的一个实施例的用于对定位数据滤波的处理设备的示意图。如图5所示，处理设备500可以包括处理器502和与处理器502耦合的存储器504。其中，存储器504存储有可执行指令，所述可执行指令当被执行时使得处理器502执行图2所示的方法200或图3所示的方法300。处理设备500可以由处理设备30或其它合适的设备来实现。Figure 5 shows a schematic diagram of a processing device for filtering positioning data in accordance with one embodiment of the present invention. As shown in FIG. 5, processing device 500 can include a processor 502 and a memory 504 coupled to processor 502. Therein, the memory 504 stores executable instructions that, when executed, cause the processor 502 to perform the method 200 illustrated in FIG. 2 or the method 300 illustrated in FIG. Processing device 500 can be implemented by processing device 30 or other suitable device.
本发明实施例还提供一种机器可读存储介质，其上具有可执行指令，当所述可执行指令被执行时，使得机器执行图2所示的方法200或图3所示的方法300。Embodiments of the present invention also provide a machine readable storage medium having executable instructions thereon that, when executed, cause a machine to perform the method 200 illustrated in FIG. 2 or the method 300 illustrated in FIG.
图6示出了按照本发明的一个实施例的定位设备的示意图。如图6所示，定位设备600可以包括定位引擎602和处理设备606。定位引擎602连续地计算目标对象的定位数据并向处理设备606输出所计算的定位数据，定位引擎602可以例如但不局限于由定位引擎20来实现。处理设备606可以例如但不局限于由处理设备500来实现。Figure 6 shows a schematic diagram of a positioning device in accordance with one embodiment of the present invention. As shown in FIG. 6, positioning device 600 can include positioning engine 602 and processing device 606. Positioning engine 602 continuously calculates positioning data for the target object and outputs the calculated positioning data to processing device 606, which may be implemented by, for example, but not limited to, positioning engine 20. Processing device 606 can be implemented, for example, but not limited to, by processing device 500.
本领域技术人员应当理解，上面所公开的各个实施例可以在不偏离发明实质的情况下做出各种变形、修改和改变，这些变形、修改和改变都应当落入在本发明的保护范围之内。因此，本发明的保护范围由所附的权利要求书来限定。It should be understood by those skilled in the art that various modifications, changes and modifications may be made without departing from the spirit of the invention. Inside. Therefore, the scope of the invention is defined by the appended claims.

Claims (17)

  1. 一种用于对定位数据滤波的方法，包括：A method for filtering positioning data, comprising:
    接收定位引擎当前时刻输出的定位数据；以及Receiving positioning data output by the positioning engine at the current moment;
    利用由两个不同的滤波器组成的交互式多模型对基于所接收的定位数据的待处理定位数据进行滤波，以得到已滤波的定位数据。The to-be-processed positioning data based on the received positioning data is filtered using an interactive multi-model consisting of two different filters to obtain filtered positioning data.
  2. 如权利要求1所述的方法，其中，所述利用由两个不同的滤波器组成的交互式多模型对基于所接收的定位数据的待处理定位数据进行滤波包括：The method of claim 1 wherein said filtering the to-be-processed positioning data based on the received positioning data using an interactive multi-model consisting of two different filters comprises:
    获取所述当前时刻的所述两个滤波器各自的初步滤波结果，其与所述待处理定位数据相关；Obtaining, respectively, a preliminary filtering result of each of the two filters at the current moment, which is related to the to-be-processed positioning data;
    计算所述当前时刻的所述两个滤波器各自的被选择概率，其中，所述当前时刻的每一个滤波器的被选择概率表示在所述当前时刻处所述交互式多模型选择该滤波器的概率；以及Calculating a selected probability of each of the two filters at the current time, wherein a selected probability of each filter of the current time indicates that the interactive multi-model selects the filter at the current time Probability;
    计算所述当前时刻的所述两个滤波器各自的初步滤波结果与被选择概率的乘积之和，作为所述已滤波的定位数据。Calculating a sum of products of respective preliminary filtering results and selected probabilities of the two filters at the current time as the filtered positioning data.
  3. 如权利要求2所述的方法，其中，所述获取所述当前时刻的所述两个滤波器各自的初步滤波结果包括：The method of claim 2, wherein the obtaining the preliminary filtering results of the two filters of the current moment comprises:
    基于所述两个滤波器各自的马尔科夫链转移概率和在所述当前时刻之前的上一时刻的所述两个滤波器各自的被选择概率，计算所述当前时刻的所述两个滤波器各自的滤波结果占比，其中，所述当前时刻的所述两个滤波器中的任一滤波器的滤波结果占比表示在所述当前时刻的所述两个滤波器的滤波结果总和中，所述当前时刻的该任一滤波器的滤波结果所占据的比例；Calculating the two filters of the current time based on a respective Markov chain transition probability of the two filters and a selected probability of each of the two filters at a previous time before the current time The respective filter result ratios of the respective filters, wherein the filter result ratio of any one of the two filters at the current time is represented by the sum of the filter results of the two filters at the current time The ratio of the filtering result of any of the filters at the current time;
    计算针对所述两个滤波器各自的混合输入，其中，针对每一个滤波器的混合输入是基于所述待处理定位数据、所述上一时刻的该滤波器的初步滤波结果、所述两个滤波器各自的马尔科夫链转移概率和所述上一时刻的所述两个滤波器各自的被选择概率而计算的；Calculating a mixing input for each of the two filters, wherein a mixing input for each filter is based on the to-be-processed positioning data, a preliminary filtering result of the filter at the last moment, the two Calculating the respective Markov chain transition probabilities of the filters and the selected probabilities of the two filters at the last moment;
    计算所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值；以及Calculating a predicted value of the positioning data for each of the two filters at the current time; and
    通过利用所述两个滤波器中的每一个滤波器对所述当前时刻的用于该滤波器的定位数据预测值进行滤波，获得所述当前时刻的所述两个滤波器各自的初步滤波结果。Obtaining a preliminary filtering result of each of the two filters at the current time by filtering the positioning data prediction value for the current time at the current time by using each of the two filters .
  4. 如权利要求3所述的方法，其中，The method of claim 3, wherein
    所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值是基于所述上一时刻的用于该滤波器的定位数据预测值、所计算的针对该滤波器的混合输入、给定且不变的转移矩阵、给定且不变的观测矩阵、以及所述当前时刻的系数增益而计算的。The positioning data prediction value for each of the two filters at the current time is based on the positioning data prediction value for the filter at the last moment, and the calculated for the filter The mixed input, the given and constant transfer matrix, the given and invariant observation matrix, and the coefficient gain of the current time are calculated.
  5. 如权利要求3所述的方法，其中，所述计算所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值包括：The method of claim 3, wherein said calculating a positioning data prediction value for each of said two filters at said current time comprises:
    计算所述当前时刻的用于所述两个滤波器中的每一个滤波器的辅助计算值，其是基于所述上一时刻的用于该滤波器的辅助计算值、所计算的针对该滤波器的混合输入、给定且不变的转移矩阵、给定且不变的观测矩阵、以及所述当前时刻的系数增益而计算的；以及Calculating an auxiliary calculation value for each of the two filters at the current time, which is based on the auxiliary calculation value for the filter at the last moment, and the calculated for the filter Calculated by the mixed input of the device, the given and constant transfer matrix, the given and invariant observation matrix, and the coefficient gain of the current time;
    确定所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值，其是基于所述待处理定位数据、所述当前时刻的用于该滤波器的辅助计算值和给定且不变的测量噪声的协方差而计算的。Determining, at the current time, a positioning data prediction value for each of the two filters, which is based on the to-be-processed positioning data, an auxiliary calculation value for the filter at the current time Calculated with the covariance of the given and constant measurement noise.
  6. 如权利要求4所述的方法，其中，所述计算当前时刻的所述两个滤波器各自的被选择概率包括：The method of claim 4, wherein said calculating a selected probability of each of said two filters at a current time comprises:
    计算针对所述两个滤波器各自的滤波参数值，其中，针对所述两个滤波器中的每一个滤波器的滤波参数值是基于所述待处理定位数据、所述当前时刻的用于该滤波器的定位数据预测值和给定且不变的测量噪声的协方差而计算的；以及Calculating a filter parameter value for each of the two filters, wherein a filter parameter value for each of the two filters is based on the to-be-processed positioning data, the current time for the Calculated by the predicted value of the positioning data of the filter and the covariance of the given and constant measurement noise;
    基于针对所述两个滤波器各自的滤波参数值和所述当前时刻的 所述两个滤波器各自的滤波结果占比，确定所述当前时刻的所述两个滤波器各自的被选择概率。Determining a selected probability of each of the two filters at the current time based on a respective filter result value for each of the two filters and a filter result ratio of each of the two filters at the current time.
  7. 如权利要求1所述的方法，还包括：The method of claim 1 further comprising:
    对所述接收的定位数据进行预处理，以得到所述待处理定位数据。Pre-processing the received positioning data to obtain the to-be-processed positioning data.
  8. 一种用于对定位数据滤波的装置，包括：An apparatus for filtering positioning data, comprising:
    接收模块，用于接收定位引擎当前时刻输出的定位数据；以及a receiving module, configured to receive positioning data output by the positioning engine at a current time;
    滤波模块，用于利用由两个不同的滤波器组成的交互式多模型对基于所接收的定位数据的待处理定位数据进行滤波，以得到已滤波的定位数据。And a filtering module, configured to filter the to-be-processed positioning data based on the received positioning data by using an interactive multi-model composed of two different filters to obtain filtered positioning data.
  9. 如权利要求8所述的装置，其中，所述滤波模块包括：The apparatus of claim 8 wherein said filtering module comprises:
    获取模块，用于获取所述当前时刻的所述两个滤波器各自的初步滤波结果，其与所述待处理定位数据相关；An obtaining module, configured to acquire a preliminary filtering result of each of the two filters at the current moment, which is related to the to-be-processed positioning data;
    第一计算模块，用于计算所述当前时刻的所述两个滤波器各自的被选择概率，其中，所述当前时刻的每一个滤波器的被选择概率表示在所述当前时刻处所述交互式多模型选择该滤波器的概率；以及a first calculating module, configured to calculate a selected probability of each of the two filters at the current moment, wherein a selected probability of each filter of the current moment represents the interaction at the current moment The probability that the multi-model selects the filter;
    第二计算模块，用于计算所述当前时刻的所述两个滤波器各自的初步滤波结果和被选择概率的乘积之和，作为所述已滤波的定位数据。And a second calculating module, configured to calculate, as the filtered positioning data, a sum of products of respective preliminary filtering results and selected probabilities of the two filters at the current moment.
  10. 如权利要求9所述的装置，其中，所述获取模块包括：The apparatus of claim 9, wherein the obtaining module comprises:
    第三计算模块，用于基于所述两个滤波器各自的马尔科夫链转移概率和在所述当前时刻之前的上一时刻的所述两个滤波器各自的被选择概率，计算所述当前时刻的所述两个滤波器各自的滤波结果占比，其中，所述当前时刻的所述两个滤波器中的任一滤波器的滤波结果占比表示在所述当前时刻的所述两个滤波器的滤波结果总和中，所述当前时刻的该任一滤波器的滤波结果所占据的比例；a third calculating module, configured to calculate the current based on a respective Markov chain transition probability of the two filters and a selected probability of each of the two filters at a previous time before the current time a filter result ratio of each of the two filters at a time, wherein a filter result ratio of any one of the two filters at the current time indicates the two at the current time a ratio of a filter result of the filter at the current time in a sum of filter results of the filter;
    第四计算模块，用于计算针对所述两个滤波器各自的混合输入，其中，针对每一个滤波器的混合输入是基于所述待处理定位数据、所述上一时刻的该滤波器的初步滤波结果、所述两个滤波器各自的马尔科夫链转移概率和所述上一时刻的所述两个滤波器各自的被选择概率而计算的；a fourth calculation module, configured to calculate a hybrid input for each of the two filters, wherein a hybrid input for each filter is based on the to-be-processed positioning data, the preliminary of the filter at the last moment Calculating a result of the filtering, a Markov chain transition probability of each of the two filters, and a selected probability of each of the two filters at the last moment;
    第五计算模块，用于计算所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值；以及a fifth calculating module, configured to calculate a positioning data prediction value for each of the two filters at the current moment;
    获得模块，用于通过利用所述两个滤波器中的每一个滤波器对所述当前时刻的用于该滤波器的定位数据预测值进行滤波，获得所述当前时刻的所述两个滤波器各自的初步滤波结果。Obtaining a module, configured to filter, by using each of the two filters, the positioning data prediction value for the current time of the filter to obtain the two filters of the current moment Their respective preliminary filtering results.
  11. 如权利要求10所述的装置，其中，The device of claim 10, wherein
    所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值是基于所述上一时刻的用于该滤波器的定位数据预测值、所计算的针对该滤波器的混合输入、给定且不变的转移矩阵、给定且不变的观测矩阵、以及所述当前时刻的系数增益而计算的。The positioning data prediction value for each of the two filters at the current time is based on the positioning data prediction value for the filter at the last moment, and the calculated for the filter The mixed input, the given and constant transfer matrix, the given and invariant observation matrix, and the coefficient gain of the current time are calculated.
  12. 如权利要求10所述的装置，其中，所述第五计算模块包括：The apparatus of claim 10 wherein said fifth computing module comprises:
    第六计算模块，用于计算所述当前时刻的用于所述两个滤波器中的每一个滤波器的辅助计算值，其是基于所述上一时刻的用于该滤波器的辅助计算值、所计算的针对该滤波器的混合输入、给定且不变的转移矩阵、给定且不变的观测矩阵、以及所述当前时刻的系数增益而计算的；以及a sixth calculation module, configured to calculate an auxiliary calculation value for each of the two filters at the current moment, which is an auxiliary calculation value for the filter based on the last moment Calculated for the mixed input of the filter, a given and invariant transition matrix, a given and invariant observation matrix, and the coefficient gain of the current time;
    第一确定模块，用于确定所述当前时刻的用于所述两个滤波器中的每一个滤波器的定位数据预测值，其是基于所述待处理定位数据、所述当前时刻的用于该滤波器的辅助计算值和给定且不变的测量噪声的协方差而计算的。a first determining module, configured to determine a positioning data prediction value for each of the two filters at the current moment, which is based on the to-be-processed positioning data, the current moment The auxiliary calculated value of the filter is calculated from the covariance of the given and constant measurement noise.
  13. 如权利要求11所述的装置，其中，所述第一计算模块包括：The apparatus of claim 11 wherein said first computing module comprises:
    第七计算模块，用于计算针对所述两个滤波器各自的滤波参数 值，其中，针对所述两个滤波器中的每一个滤波器的滤波参数值是基于所述待处理定位数据、所述当前时刻的用于该滤波器的定位数据预测值和给定且不变的测量噪声的协方差而计算的；以及a seventh calculation module, configured to calculate a filter parameter value for each of the two filters, wherein a filter parameter value for each of the two filters is based on the to-be-processed positioning data, Calculating the predicted value of the positioning data for the filter at the current time and the covariance of the given and constant measurement noise;
    第二确定模块，用于基于针对所述两个滤波器各自的滤波参数值和所述当前时刻的所述两个滤波器各自的滤波结果占比，确定所述当前时刻的所述两个滤波器各自的被选择概率。a second determining module, configured to determine the two filters of the current moment based on respective filter parameter values of the two filters and respective filter result proportions of the two filters at the current moment The respective selected probabilities.
  14. 如权利要求8所述的装置，还包括：The apparatus of claim 8 further comprising:
    预处理模块，用于对所述接收的定位数据进行预处理，以得到所述待处理定位数据。And a pre-processing module, configured to perform pre-processing on the received positioning data to obtain the to-be-processed positioning data.
  15. 一种用于对定位数据滤波的处理设备，包括：A processing device for filtering positioning data, comprising:
    处理器；以及Processor;
    存储器，其存储有可执行指令，所述可执行指令当被执行时使得所述处理器执行权利要求1-7中的任意一个所述的方法。A memory, which stores executable instructions that, when executed, cause the processor to perform the method of any one of claims 1-7.
  16. 一种机器可读存储介质，其上具有可执行指令，当所述可执行指令被执行时，使得机器执行权利要求1-7中的任意一个所述的方法。A machine readable storage medium having executable instructions thereon that, when executed, cause a machine to perform the method of any one of claims 1-7.
  17. 一种定位设备，包括：A positioning device comprising:
    定位引擎，用于连续地计算目标对象的定位数据并输出所计算的定位数据；以及a positioning engine for continuously calculating positioning data of a target object and outputting the calculated positioning data;
    如权利要求15所述的处理设备。A processing apparatus according to claim 15.
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